SOURCE-LINKED INTELLIGENCE

SAFETYFANS: SAFEty and sustainabiliTY by design: a Framework for Advanced Nano-materials Synthesis.

CORDIS · observation · Publication date unknown

to be drawn and practices to be tried. The questions above are now addressed to the Researcher, who, during her PhD, predicted the toxicity of NF based on their physicochemical characteristics using Machine Learning (ML). The team will realize a central aspect of the SSbD concept: the design, development and validation of a framework that predicts safety, sustainability, and functionality at the synthesis stage of NF, based on previous knowledge, using ML. This framework allows in-silico testing impact on SSbD of changes in NF features without conducting the actual resource-consuming experiments. The endeavor is rooted on information digitization built on exploiting the Hosts industry and academia network and using its laboratory infrastructure to deploy real cases. The project unlocks NF applicability potential, proposes a pragmatic methodology on SSbD implementation and increases public confidence towards nanotechnology and ML. Multidisciplinarity and the Hosts extroversion will distinguish the Researcher in the growing market of scientific integrators as employees and consultants

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recordType

award

status

SIGNED

region

EU

value

172750.08

unit

EUR